Real-time alerts and transmission of selected signal samples under a dynamic capacity limitation

ABSTRACT

Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

BACKGROUND

Alert systems utilize analytics models to identify event patterns in adata stream. In some examples, some identified event patterns may betagged as anomalies, and real-time alerts may be provided. In someexamples, the analytics models may be maintained at a base station,while signals may be received at a plurality of remote stations that arelinked to the base station.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating one example of asystem for real-time alerts and transmission of selected signal samples.

FIG. 2 is another example graphical illustration of a distributed systemfor real-time alerts and transmission of selected signal samples.

FIG. 3 is a block diagram illustrating one example of a computerreadable medium for real-time alerts and transmission of selected signalsamples.

FIG. 4 is a flow diagram illustrating one example of a method forreal-time alerts and transmission of selected signal samples.

FIG. 5 is a flow diagram illustrating one example of a method forproviding alerts at a production station.

FIG. 6 is a flow diagram illustrating one example of a method forproviding an incrementally updated computer generated visual renditionof an anomaly analysis at a production station.

FIG. 7 is a flow diagram illustrating one example of a method foroptimizing a selection of signal samples based on a representation ofthe signal samples by signal elements from a dictionary.

DETAILED DESCRIPTION

Analytic models are utilized in a variety of applications, including forexample, Internet of Things (“IoT”), and Interactive Analytics (“IA”).Generally, in IoT solutions, analytic models are created by a vendor andremain fixed throughout a system life. In some instances, the models aregenerated and/or maintained at a base facility that requires data totravel from a station where the data is collected, such as a remotestation, and then alerts are transmitted back to the remote station. Forexample, in the Oil and Gas (“O&G”) sector, hazard alerts aretransmitted from a base facility to platforms at sea. Such a processimposes a delay on the alert time, and may be highly susceptible tonetwork connection problems. Also some of the raw measurement data thatis to be gathered in the base station for deep analysis may get lostwhen the network connection is interrupted for an extended period.

A typical scenario for IoT is such that one or more (usually remote)nodes have limited computing power and limited network bandwidth andavailability. One example is that of O&G extraction companies, whereremote nodes may be platforms and/or stations in the ocean. Multiplesensors on the O&G production equipment generate fast streams ofmeasurement data. There is a need to process such data in real-time forimmediate hazard alerts, as well as to collect all data in a data centeraway from the drilling site for safe keeping and further short term andlong term analysis. Since the alerts can only be generated in the datacenter where the measurement data and additional data sources areavailable (seismic, weather, etc.), and where computing power issufficient for advanced statistical analysis, alerts can generally notbe provided in real-time. Hence, alerts to the offshore site aredelayed, and susceptible to network interrupts. Also, inherently limitednetwork bandwidth and availability in the offshore sites limits theirability to scale up the number, rate, and accuracy of the measurementssince the required data transfer rate would surpass the limited networkcapacity.

The framework proposed herein provides analytic functionality to avariety of IoT, DMC, and IA use cases. More specifically, it provides away to generate, maintain and apply analytics models, such as real-timealerts, in a three-tier system. The three-tier system distributed systemframework enables remote locations with limited computation, storage,and networking resources to process high-rate measurement data streamsin real-time. Accordingly, the remote locations may compute anomaliesand other complex analytics for real-time and/or forward lookingalerting, and select the right level of compressed data representationto cache and/or transmit from remote stations to the base facility,thereby mitigating the storage and/or bandwidth limitations of theremote stations.

For example, hazard alerts may be computed at a production station (or aremote/off station, or an oil rig at sea) in real-time, based on modelsmaintained and/or generated at a base facility. In some examples, themodels may be generated/updated at a global center based on datareceived from a plurality of base facilities and a plurality ofproduction stations. This way, the anomaly computation may continueuninterrupted at the production station when the network (between theproduction station and the base facility) is interrupted, even forrelatively extended periods of time. Additionally, smart dataeconomization may be achieved based on the anomaly level. For example,redundant data may be discarded and/or compressed at the source in theremote station, thereby mitigating storage and/or network bandwidthlimitations. Accordingly, the system may support a larger number ofsensors and higher measurement rates without needing to invest in costlyremote networking solutions.

As described in various examples herein, real-time alerts andtransmission of selected signal samples is disclosed. One example is asystem including a base facility linked to a production station with analerting system to perform anomaly analysis utilizing an anomaly model.A receiver at the base facility receives, from the production station, aselection of signal samples based on the anomaly analysis, where thereceived selection is optimized at the production station to besubstantially relevant to an update of a statistical model whileadhering to a dynamic capacity limitation of the production station. Thestatistical model is maintained at the base facility and incorporatesfeatures related to the production station. A management system at thebase facility updates the statistical model based on the receivedselection, optionally derives an updated anomaly model based on thestatistical model, and optionally transmits the updated anomaly model tothe production station.

The proposed distributed framework utilizes algorithms coupled withhardware at each one of a two-tier computational system (e.g., a remotenode such as the production station, and an operations base such as thebase facility), and their interconnections, in order to enable analyticmodel creation and consumption. In some examples, the distributedframework utilizes algorithms coupled with hardware at each one of athree-tier computational system (e.g., a remote node such as theproduction station, an operations base such as the base facility, and ahigh compute big data global center such as a global processing center),and their interconnections, in order to enable the analytic modelcreation and consumption. More specifically, several features describedherein take the system described out of the realm of a general purposecomputer that performs generic functions. For example, computerfunctionality is significantly enhanced via various described features,including at least features such as an algorithm to enable the cycle ofeconomizing data to be transmitted from the remote node to theoperations base using real-time data analysis at the remote node,utilization of the big data global center for model computation based onthe transmitted data, utilization of the operations base for trackingresults and maintaining appropriate models, and application of theanomaly model at the remote node. The proposed framework also providesan improvement to the technological field of real-time analytics bysolving concrete problems that arise in the context of limited and/ordynamic capacity limitations of computer systems.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific examples in which the disclosure may bepracticed. It is to be understood that other examples may be utilized,and structural or logical changes may be made without departing from thescope of the present disclosure. The following detailed description,therefore, is not to be taken in a limiting sense, and the scope of thepresent disclosure is defined by the appended claims. It is to beunderstood that features of the various examples described herein may becombined, in part or whole, with each other, unless specifically notedotherwise.

FIG. 1 is a functional block diagram illustrating one example of asystem 100 for real-time alerts and transmission of selected signalsamples. System 100 includes a base facility 106, comprising a receiver108, and a management system 110. The base facility 106 is linked to aproduction station 102. Generally, the production station 102 is a localand/or remote station where real-time data is collected, processed, andreal-time hazard alerts are provided. For example, the productionstation 102 may be a remote oil rig located in the ocean, a seismicdevice located at a location to detect seismic activity, or a satellite.The production station 102, as described herein, has a capability tointelligently select portions of real-time data that may need to bestored, processed, or sent over to a central command and controlfacility for deeper analysis. Generally, the production station 102 haslimited computing and/or network resources. The base facility 106 is acommand and control facility linked to the production station 102, andin some examples, to a plurality of production stations. The basefacility 106 is equipped with comparatively greater computing resourcesthan the production station 102, to receive the selected portions of thereal-time data from the production system 102, analyze the receiveddata, generate statistical and other anomaly and/or pattern detectionmodels, incorporate data related to weather conditions, geographicalconditions, resource allocations, equipment data, and so forth, toprovide the production station 102 with a capability to maintain and runthe real-time hazard alerting system, that may continue to analyzereal-time data and provide relevant alerts even in an absence of acommunication with the base facility 106. These and other features ofsystem 100 are described herein.

Generally, remote production locations have limited computing power andworking memory capacity, and hence are not equipped for the complexintensive data analysis needed to transform the sensor data streams intodetailed statistical models that are needed for detecting or predictinganomalies indicative of potential hazards or production inefficiencies.In addition, due to limited long term storage and limited networkbandwidth and availability, the remote production location may have verylimited access to its own historical data, to historical data from otherremote production locations, or to other data on environmentalconditions which need to be factored into the statistical model (e.g.seismic, weather). Generally, in order to overcome these limitations,the remote production stations are equipped to receive alerts onpotential hazards or production inefficiencies based on communicationwith a base station, such as the base facility 106, which does not havethe capacity limitations of the production station 102. For example, theremote production station 102 receives the signals from the localsensors, transmits them to the base facility 106, where they aregathered and analyzed optionally in conjunction with historical data orenvironmental data. The base facility 106 detects anomalies based on theanalysis alerts and transmits appropriate alerts back to the remoteproduction station 102. This method of overcoming the capacitylimitations of the remote production station 102, makes the alertingdependent on communication with the base facility 106.

However, in many situations, as a result of limited networkavailability, the remote production station 102 might lose communicationwith the base facility 106 for extended periods of time, effectivelyleaving the remote production station 102 without a capability togenerate alerts. In addition, the remote production locations mayreceive multiple real-time sensor signals at a high rate, and theirreduced data storage and signal transmission capacities may not be ableto keep up with the high rate of incoming data. The data storage problemmay be further aggravated at network down times. Accordingly, signalsamples may be lost, and some signals may not be processed or analyzed.In particular, during critical times of safety events when there is anincreased risk of communication loss, there may also be an increasedrisk of losing data that may be critical to infer a cause of a safetyevent in retrospect. In addition, data loss at the remote productionstation 102 may impact the quality of signal analysis and statisticalmodeling at the base facility 106, and reduce the quality of anomalydetection, so significant alerts may not be provided. Therefore, it isdesirable to implement a system where the production station 102 mayhave the capacity to continuously analyze the incoming signals, andprovide alerts, even with limited computing power, and optionally in anabsence of a network connection for prolonged times, and/or low networkbandwidth in a communication link with the base facility 106.

As described herein, the production station 102 includes a sensor 104Ato receive real-time signals. The sensor 104A may be located at theproduction station 102, or in a proximity of the production station 102.As described herein, the production station 102 is equipped with analerting system 104B to perform anomaly analysis on the real-timesignals received by the sensor 104A, and where the anomaly analysisutilizes an anomaly model. In some examples, the production station 102provides alerts based on the detected event patterns. The alertingsystem 1046 has a capability to perform anomaly analysis on thereal-time signals. Generally, the alerting system 1046 may functionunder dynamic capacity limitations of the production station 102. Forexample, the alerting system 104B may function even when the link withthe base facility 106 is not active. For example, a network that linksthe production station 102 and the base facility 106 may be interrupted,have low bandwidth, and/or there may be a network failure. However, thealerting system 104B may continue to perform anomaly analysis andprovide alerts based on the existing anomaly model.

System 100 includes the receiver 108 at the base facility 106 toreceive, from the production station 102, a selection of signal samplesbased on the anomaly analysis, where the received selection is optimizedat the production station 102 to be substantially relevant to an updateof a statistical model while adhering to a dynamic capacity limitationof the production station 102, and where the statistical model ismaintained at the base facility 106 and incorporates features related tothe production station 102. For example, the selection of signal samplesmay be optimized to adhere to a storage capacity limitation, so thatsignal samples that are substantially relevant to a model update may bestored. Also, for example, the selection of signal samples may beselected to adhere to a limited network capacity, so that signal samplesthat are substantially relevant to a model update may be selected fortransmission to the base facility 106. As another example, the selectionof signal samples may be selected to adhere to a dynamic networkbandwidth, so that a smaller collection of signal samples are providedto the base facility 106 when the available network bandwidth is low,and the smaller sample is selected based on relevance to an update ofthe statistical model. Also, for example, the selection of signalsamples may be selected to adhere to a dynamic incoming rate ofreal-time signals, so that signal samples that are substantiallyrelevant to a model update may be selected at a rate that is consistentwith the rate of incoming real-time signals.

Generally, the statistical model at the base facility 106 consists of asummarization of selected features of the signal samples, for example inthe form of a data clustering model, and the production station 102identifies the selection of signal samples based on the anomalyanalysis. Signal samples measured at times where the overall anomalyscores are high bear the most significant information both for alertingand for updating the overall statistical model of the signal, since theycorrespond to a situation that is not common (by definition of“anomaly”), and hence not well represented in the statistical model (notsubstantially similar to any data cluster). In some examples, adheringto the dynamic capacity limitation of the production station 102 maygenerally mean that such high anomaly samples get preference fortransmission to the base facility 106 and caching in the remoteproduction station 102.

Signal samples measured at times where the overall anomaly score is lowbelong in fact to data-clusters already significantly represented in thestatistical model. Hence, these samples may be subject to selective datareduction or data selection depending on which parts of the signalsample are determined by the anomaly model to be more common. In someexamples, adhering to the dynamic capacity limitation of the productionstation 102 may generally mean such selective reduction or dataselection. For example, if the anomaly analysis includes measuring thesimilarity of a signal sample to common data clusters, then the datareduction may consist of identifying the most similar cluster. If theanomaly analysis does not identify a similar cluster, the data reductionmay include signal sub-sampling, signal quantization or featuresummarization. Overall, the production station dynamically optimizes atrade-off between representation accuracy and size of the selected dataso it adheres to the dynamic network capacity and data caching capacityin the production station 102, while remaining substantially relevant tothe update of the statistical model to cover parts of the signaldistribution that may not be represented well in the model yet.

The dynamic capacity limitation of the production station 102 mayinclude limited computing power, working memory capacity, long termstorage capacity, network bandwidth, network failure, network downtime,and/or other computing power, infrastructure, and/or network relatedmeasurements. The term dynamic generally refers to a potential change incomputing and/or network resources. For example, a large number ofsignals may be analyzed during a first interval of time and may resultin lower computing power, lower working memory capacity, and so forth.Also, for example, the network may be intermittent. As another example,the dynamic capacity limitation may be a fluctuating network bandwidth.As another example, a network failure may result in a large amount ofdata not being transferred from the production station 102 to the basefacility 106, thereby leading to decreased storage capacity. A keyaspect of system 100 is an ability to continue to perform an effectiveanomaly analysis under limited computing resources, while being able tosend relevant data over to the base facility 106 for deeper analysis andpotential updates.

For example, the capacity limitation may be a network failure causing aloss of the communication link between the production station 102 andthe base facility 106. However, equipped with the alerting system 104B,the production station 102 may apply the anomaly model to detectanomalies in the real-time signals even in the absence of thecommunication link with the base facility 106. The production station102 may identify the selection of signal samples, where the selectionadheres to changes in data cache capacity caused by the network failure.Accordingly, the production station 102 may identify when the network isrestored, and may provide the selection to the base facility 106 whenthe network is restored. Accordingly, a continuous and updated hazardalert system is deployed, and historical signal data that is identifiedas relevant is saved and transmitted to the base facility 106.

In some examples, due to the capacity limitation and a relatively highdata rate, the temporal features extractors from the signals may belimited to low complexity short window or recursive operators. In someexamples, detected event patterns may be cached and transmitted togetherwith the raw signal samples to the base facility 106 for storage andstatistical modeling.

Generally, raw measurements may be recorded at a higher rate thannecessary at most times, in order to be able to respond to physicalphenomena on the shortest time scale of interest, but these phenomenamay occur in extraordinary conditions, i.e. when there are anomalies insome of the measurements. At these times, it is important to transmit tothe base facility 106, the complete raw signal for subsequent analysisbeyond just anomaly modeling. However, at many times when there are nosignificant anomalies, transmitting a full resolution signal may beunnecessary, as the relevant information is sufficiently contained in asub-sampled version of the raw signal, and in the extracted signalfeatures and/or event patterns, and generally with lower data rate thanthe raw signals in their entirety.

As described herein, the received selection may be optimized at theproduction station 102 to be substantially relevant to an update of thestatistical model while adhering to the dynamic capacity limitation ofthe production station 102. In some examples, the production station 102may include an anomaly handling block (not shown) that caches andanalyzes a stream of anomaly scores for a plurality of detectedanomaly-event patterns. Unlike a high data rate of the raw measurementstreams, the extracted anomaly-score streams are sparse (e.g., zero mostof the time), and hence highly compressible. In some examples, theextracted anomaly-score streams may be transformed into a stream ofisolated anomaly events, each tagged by measurement type, event patterntype, and anomaly score and a timestamp. This anomaly-event stream maybe selected to conform to a low data rate, thereby allowing localcaching of extended history periods, even under significant storagelimitations at production stations.

Also, for example, since the anomaly scoring on each individual eventpattern occurs in real-time, it may be possible to dynamically combinethe anomaly scores in a variety of ways to generate specific anomalydetectors for system-wide hazards or sub-system related hazards.Participating event patterns in each anomaly, their combination weights,and the alert threshold, may be controlled dynamically either from thebase facility 106, or locally at the production station 102. This mayreduce potential volatility of the hazard alert system to networkintermissions.

Also, for example, a significant benefit of the fact that the anomalyscores may be computed at the production station 102 in real-time isthat they may be utilized to select the times at which raw measurementsamples are important to cache and transmit in full detail, anddetermine the times at which a low detail summary is sufficient.Accordingly, the selection may be optimized to be substantially relevantwhile adhering to the dynamic capacity limitation. For example, areal-time selectivity mechanism may significantly reduce, on average, arate of raw signal data to be cached at the production station 102, andtransmitted to the base facility 106, thus mitigating both the storageand network limitations of the production station 102.

In some examples, the practical benefits of the data selection schemedescribed herein may be that more signal streams may send the relevantdata over the same limited network bandwidth. Also, significantly longerhistory of relevant data may be cached at the production station 102under the same capacity limitations (e.g., storage limitations), whichmay increase resiliency against long network intermissions, where datain a cache at the production station 102 may be overwritten if notalready transmitted to the base facility 106.

The statistical model, as described herein, may be generally maintainedat the base facility 106 and may measure signals, and/or properties ofsignals received from the production station 102. The term “maintain” asused herein may generally refer to a variety of computing functionsincluding generating, and/or updating the statistical model,incorporating conditions data, storing and/or updating a database tostore aspects of the model, retrieving data from the database,maintaining communication links and/or network protocols with computingdevices in a distributed framework so as to provide models derived fromthe statistical model, and so forth. Maintaining the statistical modelmay generally require larger computing resources than what may beavailable at the production center 102. Also, for example, the basefacility 106 may be linked to a plurality of production stations andreceived signals from the plurality of production stations. In suchexamples, the statistical model may incorporate signals and/orproperties of signals received from all these production stations.Generally, the statistical model receives the selection from theproduction station 102 via the receiver 108, and analyzes the receivedselection. Also, for example, the statistical model may not be capableof performing the anomaly analysis. However, the anomaly model performsthe anomaly analysis, and may be derived from the statistical model.

The base facility 106 is generally a regional station that communicateswith a plurality of production stations. Data is gathered from remotestations, and statistical models are computed for tactical purposes suchas hazard alerts and production monitoring. The base facility 106generally has fewer limitations of compute power, storage, or network,and is equipped to perform significantly extensive data analytics.Generally, it has the ability to gather data, analyze it, detectpatterns, and design appropriate models that may be suitable to beoperable at the different production stations, based on their respectivecapacity limitations. The framework of the base facility 106 is targetedtowards optimized signal representations and data size economizations tomake system 100 more scalable.

System 100 includes a management system 110 at the base facility 106 toupdate the statistical model based on the received selection. In someexamples, the management system 110 may configure the alerting system1046 to detect anomaly events in the real-time signals, and where thereceived selection includes a selection of the detected anomaly events.In some examples, the statistical model may include an anomaly eventpatterns model, and the management system 110 may update the anomalyevent patterns model based on the received selection. In some examples,the anomaly events may also be computed separately at the productionstation 102 and cached there as well.

In some examples, the management system 110 may configure the alertingsystem 1046 to determine anomaly scores for detected event patterns, andupdate the anomaly scoring models based on statistical analysis of thehistory of the observed data. In some examples, the management system110 may configure the alerting system 104B to apply anomaly patternalerting rules to provide alerts at the production station, and wherethe management system 110 may update the alerting rules based on theupdated anomaly event patterns model. In some examples, thedetermination of the anomaly scores may be performed in real-time. Theanomaly scoring models may be updated when enough additional data hasbeen gathered, or when external conditions that influence the anomalyscore determination change sufficiently. Once sufficient statistics havebeen gathered at the base facility 106 for a variety of externalconditions (e.g., weather, humidity, seismic), the anomaly scores changeslowly in time. Accordingly, the real-time anomaly score determinationis sufficiently accurate even if the anomaly scoring models included inthe existing anomaly model at the production station 102 are somewhatoutdated.

The management system 110 generates or updates the statistical modelbased on the anomaly detection rules, where the statistical modelincorporates features related to the production station 102. Asdescribed herein, the statistical model is generally a model based onhistory of data collected over time from a particular production station102. Features related to the production station 102 may generally referto any data that may be relevant to the operation or functioning of afacility at the production station 102. For example, operation of an oilrig at the sea may be influenced by local features such as temperatureand humidity conditions, seismic conditions, tidal patterns, windconditions, and so forth. Also, for example, the features may includeany relevant data related to machinery, computer systems, and so forththat are pertinent to the production station 102. In some examples, thefeatures may include parameters of the capacity limitation of theproduction station 102.

The anomaly events may be analyzed at the base facility 106 by standarddata mining techniques to find recurring event patterns (basket analysisfor simultaneous anomalies, and sequence analysis for temporal anomalypatterns). Subject matter experts (SMEs) at the base facility 106 maytag event patterns indicative of known problem types and create anomalydetection rules for higher level event pattern detection. These anomalydetection rules may be transmitted to the production station 102 overthe network, stored and updated at the production station 102 forreal-time application to the anomaly event stream incoming from theanomaly scoring system. Again, these anomaly detection rules may beapplied at the production station 102 without interruption, even iftheir updates lag due to network intermissions.

Generally, the anomaly detection algorithms may need to consider a timerelevance of the models sent from the base facility 106, in casecommunications are down for a while. Nevertheless, in many examples, amodel that was generated several hours ago may be almost as relevant asa recently updated model, and may be more relevant than a model thatdoes not account for historical signal data at all.

In some examples, system 100 includes a graphical user interface (notshown in the figures) to provide, via a computing device at the basefacility 106, an incrementally updated computer generated visualrendition of the selection of received selection. For example, areal-time signal series may be provided, with signal samples, eventpatterns, anomalies, anomaly scores, etc. displayed along with thesignal series. In some examples, the graphical user interface mayreceive, via the computing device, an indication to tag a signal sampleor event pattern as relevant. For example, the SME may provide anindication to tag certain event patters as relevant. Also, for example,a machine learning system may automatically provide an indication to tagcertain event patters as relevant based on labeled training data. Insome examples, the management system 110 may create the anomaly patternalerting rules based on the indication.

In some examples, the base facility 106 may be linked to more than oneproduction station 102, such as a second production station (not shownin the figures), and the management system 110 may update a secondstatistical model that incorporates features related to the secondproduction station. The update of the second statistical model may bebased on a second received selection from the second production station,and the second received selection may be optimized at the secondproduction station to be substantially relevant to an update of thesecond statistical model, while adhering to a second dynamic capacitylimitation of the second production station.

For example, the receiver 108 may receive a second selection of signalsamples from the second production station, where the second selectionis based on additional real-time signals received by a second sensor atthe second production station, and where the second selection adheres toan additional capacity limitation at the second production station. Forexample, the second production station may be a remote oil rig subjectto different features (equipment, weather conditions, etc.) and may besubject to a separate set of limitations with respect to data storagecapacity, rate at which incoming signal data is received, processingspeeds, computing power, network bandwidth, and so forth. Accordingly,the second selection may be tailored to adhere to such an additionalcapacity limitation of the second production station.

In some examples, the management system 110 creates additional anomalydetection rules based on the second selection, and generates or updatesa second statistical model based on the additional anomaly detectionrules, where the second statistical model incorporates features relatedto the second production station and global features related to theproduction station 102 and the second production station. For example,the management system 110 may incorporate features that are specific toeach production station, and also identify and incorporate globalfeatures that may be relevant to more than one production station. Forexample, a first collection of production stations may be seismiccounters located near regions with potential volcanic activity, whereasa second collection of production stations may be seismic counterslocated near oceanic regions with potential tsunami activity.Accordingly, although the local feature may be conditions relevant tothe individual seismic counters, a first set of global features may beidentified for the first collection of production stations based atleast in part on their proximity to regions with potential volcanicactivity. Likewise, a second set of global features may be identifiedfor the second collection of production stations based at least in parton their proximity to regions with potential tsunami activity.

Accordingly, different statistical models may be configured and/orupdated for each of the production stations. In some examples, thestatistical model may incorporate only local features, or only globalfeatures, or a combination of both. The management system 110 generatesand/or updates small statistical models (e.g. PDFs or short timepredictors conditional on operating conditions at a local productionstation), and transmit them once every period of time. Such models forma baseline for anomaly detection and/or detection of event patterns andreal-time analysis of the streaming data at the production station 102.Although the anomaly models may be generated, and/or derived at theproduction station 102, such models may be inadequate for a lack ofhistorical signal data (based on limited data storage capacities).

In some examples, the base facility 106 receives conditions dataincluding weather data from an online provider, and the managementsystem 110 incorporates the conditions data into the statistical model.For example, operation of an oil rig at the sea may be influenced byfeatures such as temperature and humidity conditions at the oil rig,seismic conditions, tidal patterns, wind conditions, and so forth. Themanagement system 110 incorporates such conditions data into thestatistical model.

In some examples, the management system 110 derives an updated anomalymodel based on the statistical model when it is determined that theanomaly model is to be updated. As described herein, the managementsystem 110 generates or updates small statistical models and/or smallsize anomaly maps, that may be derived from the statistical model, forthe signal features and/or event patterns, and transmits these anomalymaps over the network back to the production station 102.

In some examples, the management system 110 may determine if the anomalymodel is to be updated based on the updated statistical model, where thedetermination is based on changes in the statistical model. For example,the received selection may be indicative of new signal samples, and thestatistical model may be updated based on the new signal samples. Themanagement system 110 may determine if the existing anomaly model in thealerting system 104B at the production station 102 is equipped to detectthe new signal patterns. In some examples, the management system 110 maydetermine that the existing anomaly model at the production station 102is equipped to detect the new signal patterns. Accordingly, it may notderive an updated anomaly model. In some examples, the management system110 may determine that the existing anomaly model at the productionstation 102 is not equipped to detect the new signal patterns.Accordingly, it may derive an updated anomaly model.

In some examples, the management system 110 transmits the updatedanomaly model to the production station when it is determined that thecapacity limitation allows the transmit. For example, based on thecapacity limitations and differences between the existing anomaly modelat the production station 102, and the updated anomaly model derived atthe base facility 106, the management system 110 may determine if theupdated anomaly model is to be provided to the production station 102.In some examples, such a determination may be based on a number offactors. For example, availability of a higher network bandwidth mayprompt the management system 110 to derive an updated anomaly model.Also, for example, the management system 110 may determine that thecomputing resources available at the production station 102 haveincreased, and may determine that an updated anomaly model may betransmitted. As another example, conditions data may be indicative of animpending severe weather condition, and the management system 110 maydetermine that an updated anomaly model may need to be transmitted tothe production station 102 that is likely to be potentially impacted bythe severe weather condition. In some examples, the management system110 may automatically update the statistical model periodically, derivean updated anomaly model, and transmit it to the production station 102.

In some examples, similar production stations may be grouped together ina cluster based on a similarity metric, and statistical models may beaggregated from similar production stations. Generally, defining suchsimilarity may require domain knowledge. In some examples, system 100may receive, via a computing device at the base facility 106, input fromSME to determine the similarity, and/or identify the clusters ofproduction stations. Accordingly, system 100 may provide real-timehazard estimation while still accounting for historical data from aplurality of production stations.

In some examples, the base facility 106 may be linked to a globalprocessing center (not shown in the figures). Generally, the globalprocessing and/or analytics center is where all data from a plurality ofbase facilities and a plurality of production centers may be gathered,and global statistical models may be computed for strategic proposes(e.g., global modeling for optimized alert accuracy and datacompression, global production monitoring and optimization, etc.).

For example, the global processing center may collect raw data samplesfrom the production stations via intermediary regional base facilities.The global center is generally equipped with high performance computingcapabilities and can run for example, deep learning algorithms on signalsamples gathered from a plurality of production stations. An overcomplete dictionary of basic signal elements may be learnt from thegathered data, either by auto encoder layers of deep networks, or byanother high end dictionary learning method. In some examples, similarproduction stations may be grouped together in a cluster based on asimilarity metric, and the dictionary may be generated for the clusterbased on the plurality of event patterns and signal samples receivedfrom the production stations in the cluster.

In some examples, the receiver 108 in the base facility 106 may receivea dictionary of signal elements from the global processing center, wherethe dictionary is generated by the global processing center based ondeep learning algorithms performed on a plurality of event patterns andsignal samples received from a plurality of production stations via aplurality of base facilities. In some examples, the management system110 may utilize the dictionary to create and/or update the anomalydetection rules, and utilize the dictionary to generate and/or updatethe statistical model. In some examples, the management system 110provides the dictionary to the production station 102, and theproduction station 102 optimizes the received selection based on arepresentation of the signal samples by the signal elements, where anerror in the representation of the signal sample is small if the signalsample is substantially represented as a combination of a small numberof the signal elements.

For example, a signal snippet may be identified in the real-time signalsat the production station 102, and the signal snippet may be representedas a combination of few signal elements from the dictionary. Generally,a majority of raw signal snippets may have accurate sparserepresentations as combinations of few dictionary elements, and a verysmall residual that is not represented well by any other dictionaryelement. Accordingly, a signal novelty scoring system (not shown in thefigure) may determine a signal novelty score for the signal snippetbased on an error in the approximating. In some examples, each signalsnippet may be represented with a small number of signal elements fromthe dictionary such that remaining residual is below a threshold. If thesignal snippet is novel, i.e. not well represented by existing signalelements from the dictionary, leaving a large residual, then the noveltyscore is determined to be high. In some examples, a signal snippet isincluded in the selection of signal samples if the signal novelty scoreis determined to exceed a threshold. Such signal snippets with highnovelty scores may be transmitted in their entirety to the base facility106 and from there to the global processing center, to be learnt by thesignal dictionary deep learner.

As described herein, system 100 may be implemented by a computingdevice. As used herein, a computing device may be a desktop computer,laptop (or notebook) computer, workstation, tablet computer, mobilephone, smart device, switch, router, server, blade enclosure, or anyother processing device or equipment including a processing resource. Inexamples described herein, a processing resource may include, forexample, one processor or multiple processors included in a singlecomputing device or distributed across multiple computing devices. Thecomponents of system 100 (e.g., 106, 108, and 110) may be anycombination of hardware and programming to implement the functionalitiesdescribed herein. In examples described herein, such combinations ofhardware and programming may be implemented in a number of differentways. For example, the programming for the components may be processorexecutable instructions stored on at least one non-transitorymachine-readable storage medium and the hardware for the components mayinclude at least one processing resource to execute those instructions.In some examples, the hardware may also include other electroniccircuitry to at least partially implement at least one component ofsystem 100. In some examples, the at least one machine-readable storagemedium may store instructions that, when executed by the at least oneprocessing resource, at least partially implement some or all ofcomponents 106, 108, and 110 of system 100. In such examples, system 100may include the at least one machine-readable storage medium storing theinstructions and the at least one processing resource to execute theinstructions. In other examples, the functionalities of any componentsof system 100 may be at least partially implemented in the form ofelectronic circuitry.

For example, the receiver 108 may be a combination of hardware andprogramming (e.g., processor executable instructions) to receive, at thebase facility 106, a selection of signal samples from the productionstation 102. For example, the programming of receiver 108 may includeinstructions executable to automatically receive the selection of signalsamples. Also, for example, receiver 108 may include hardware tophysically store, for example, the selection of signal samples. Also,for example, receiver 108 may include a combination of hardware andsoftware programming to dynamically interact with the other componentsof system 100.

Likewise, the management system 110 may be a combination of hardware andprogramming (e.g., processor executable instructions) to update thestatistical model based on the received selection. Also, for example,the programming of management system 110 may include instructionsexecutable to automatically generate or update a statistical model basedon the anomaly detection rules, where the statistical model incorporatesfeatures related to the production station 102. As another example, theprogramming of management system 110 may include hardware to physicallystore, for example, the statistical models. The management system 110may include a combination of hardware and software programming todynamically interact with the other components of system 100.

Likewise, the production station 102 may be a combination of hardwareand programming (e.g., processor executable instructions) toautomatically receive real-time signals via a sensor at the productionstation 102, where the production station 102 is associated with adynamic capacity limitation. As another example, the programming ofproduction station 102 may include instructions to automaticallyperform, via an alerting system, anomaly analysis on real-time signalsreceived by a sensor, where the anomaly analysis utilizes an anomalymodel. Also, for example, the programming of production station 102 mayinclude instructions to automatically identify, at the productionstation 102, a selection of signal samples and detected event patterns,where the selection adheres to a dynamic capacity limitation of theproduction station 102. The production station 102 may include acombination of hardware and software programming to dynamically interactwith the other components of system 100.

Generally, as described herein, the components of system 100 may includesoftware programming and physical networks to be linked to othercomponents of system 100. In some instances, the components of system100 may include a processor and a memory, while programming code isstored on that memory and executable by a processor to performdesignated functions.

A computing device, as used herein, may be, for example, a web-basedserver, a local area network server, a cloud-based server, a notebookcomputer, a desktop computer, an all-in-one system, a tablet computingdevice, a mobile phone, an electronic book reader, or any otherelectronic device suitable for provisioning a computing resource toperform a unified visualization interface. The computing device mayinclude a processor and a computer-readable storage medium.

FIG. 2 is another example graphical illustration of a distributed system200 for real-time alerts and transmission of selected signal samples.Distributed system 200 comprises a global processing center 202 linkedto a plurality of base facilities, such as base facility A 204A, basefacility B 204B, . . . , base facility T 204T. Each base facility islinked to a plurality of production stations. For example, base facilityA 204A is linked to a plurality of production stations such asproduction station A1 206(1), production station A2 206(2), . . . ,production station AX 206(X). As another example, base facility B 204Bis linked to a plurality of production stations such as productionstation B1 208(1), production station B2 208(2), . . . , productionstation BY 206(Y). Also, for example, base facility T 204T is linked toa plurality of production stations such as production station T1 210(1),production station T2 210(2), . . . , production station TZ 210(Z).

As described herein, base facility A 204A may generate and update afirst statistical model based on a first set of features that arerelevant to production station A1 206(1); a second statistical modelbased on a second set of features that are relevant to productionstation A2 206(2), and so forth. Also, for example, base facility A 204Amay generate and update some of the statistical models based on globalfeatures relevant to more than one production station. In some examples,the global processing center 202 may generate a dictionary of signalelements based on data received from production station A1 206(1),production station A2 206(2), . . . , production station AX 206(X),production station B1 208(1), production station B2 208(2), . . . ,production station BY 206(Y), and production station T1 210(1),production station T2 210(2), . . . , production station TZ 210(Z). Eachof these production stations may receive the dictionary of signalelements, identify and score signal snippets in real-time signals, anddetermine if a signal snippet is to be discarded, or saved and/orprovided to a respective base facility for further analysis. Once newsignal snippets arrive at the base facilities, these may be forwarded tothe global processing center 202 so that the dictionary of signalelements may be updated. Although not illustrated in FIG. 2, aproduction station may be linked to more than one base facility.

FIG. 3 is a block diagram illustrating one example of a computerreadable medium for real-time alerts and transmission of selected signalsamples. Processing system 300 includes a processor 302, a computerreadable medium 308, input device 304, and output device 306. Processor302, computer readable medium 308, input device 304, and output device306 are coupled to each other through a communication link (e.g., abus).

Processor 302 executes instructions included in the computer readablemedium 308. Computer readable medium 308 includes signal receiptinstructions 310 to receive real-time signals via a sensor at aproduction station, wherein the production station is associated with adynamic capacity limitation.

Computer readable medium 308 includes anomaly analysis instructions 312to perform, via an alerting system, anomaly analysis on real-timesignals received by a sensor, wherein the anomaly analysis utilizes ananomaly model.

Computer readable medium 308 includes selection identificationinstructions 314 to identify, at the production station, a selection ofsignal samples based on the signal analysis, wherein the selection isoptimized at the production station to be substantially relevant to anupdate of a statistical model while adhering to the dynamic capacitylimitation of the production station, and wherein the statistical modelis maintained at a base facility linked to the production station, andwherein the statistical model incorporates features related to theproduction station.

Computer readable medium 308 includes selection providing instructions316 to provide the selection to the base facility, wherein the basefacility updates the statistical model based on the received selection,and derives an updated anomaly model based on the statistical model whenit is determined that the anomaly model is to be updated.

Computer readable medium 308 includes updated model receipt instructions318 to receive the updated anomaly model from the base facility toperform anomaly analysis, when it is determined that the capacitylimitation allows the production station to receive the updated anomalymodel.

Computer readable medium 308 includes alert rules applicationinstructions 320 to utilize the anomaly alerting rules to apply anomalypattern alerting rules.

Computer readable medium 308 includes alert providing instructions 322to provide alerts at the production station, wherein the alerts aregenerated based on the anomaly pattern alerting rules.

In some examples, computer readable medium 308 includes instructions toreceive a dictionary of signal elements from a global processing centerlinked to the base facility, instructions to identify a signal snippetof the received real-time signals, instructions to approximate thesignal snippet as a combination of the signal elements, instructions todetermine a signal novelty score for the signal snippet based on anerror in the approximating, and instructions to include the signalsnippet in the selection of signal samples if the signal novelty scoreexceeds a threshold.

Input device 304 may include a sensor, keyboard, mouse, data ports,and/or other suitable devices for inputting information into processingsystem 300. In some examples, input device 304, such as a sensor, may beutilized to receive the real-time signals via the sensor. In someexamples, input device 304, such as a computing device, may be utilizedto receive an indication to tag a signal sample or event pattern asrelevant. Output device 306 may include a monitor, speakers, data ports,and/or other suitable devices for outputting information from processingsystem 300. In some examples, output device 306 may be utilized toprovide, via a computing device at the base facility, an incrementallyupdated computer generated visual rendition of the selection of signalsamples. In some examples, output device 306 may be utilized to provide,in real-time via a computing device at the production station, anincrementally updated computer generated visual rendition of thedetected event patterns.

As used herein, a “computer readable medium” may be any electronic,magnetic, optical, or other physical storage apparatus to contain orstore information such as executable instructions, data, and the like.For example, any computer readable storage medium described herein maybe any of Random Access Memory (RAM), volatile memory, non-volatilememory, flash memory, a storage drive (e.g., a hard drive), a solidstate drive, and the like, or a combination thereof. For example, thecomputer readable medium 308 can include one of or multiple differentforms of memory including semiconductor memory devices such as dynamicor static random access memories (DRAMs or SRAMs), erasable andprogrammable read-only memories (EPROMs), electrically erasable andprogrammable read-only memories (EEPROMs) and flash memories; magneticdisks such as fixed, floppy and removable disks; other magnetic mediaincluding tape; optical media such as compact disks (CDs) or digitalvideo disks (DVDs); or other types of storage devices.

As described herein, various components of the processing system 300 areidentified and refer to a combination of hardware and programmingconfigured to perform a designated visualization function. Asillustrated in FIG. 3, the programming may be processor executableinstructions stored on tangible computer readable medium 308, and thehardware may include processor 302 for executing those instructions.Thus, computer readable medium 308 may store program instructions that,when executed by processor 302, implement the various components of theprocessing system 300.

Such computer readable storage medium or media is (are) considered to bepart of an article (or article of manufacture). An article or article ofmanufacture can refer to any manufactured single component or multiplecomponents. The storage medium or media can be located either in themachine running the machine-readable instructions, or located at aremote site from which machine-readable instructions can be downloadedover a network for execution.

Computer readable medium 308 may be any of a number of memory componentscapable of storing instructions that can be executed by Processor 302.Computer readable medium 308 may be non-transitory in the sense that itdoes not encompass a transitory signal but instead is made up of one ormore memory components configured to store the relevant instructions.Computer readable medium 308 may be implemented in a single device ordistributed across devices. Likewise, processor 302 represents anynumber of processors capable of executing instructions stored bycomputer readable medium 308. Processor 302 may be integrated in asingle device or distributed across devices. Further, computer readablemedium 308 may be fully or partially integrated in the same device asprocessor 302 (as illustrated), or it may be separate but accessible tothat device and processor 302. In some examples, computer readablemedium 308 may be a machine-readable storage medium.

FIG. 4 is a flow diagram illustrating one example of a method forreal-time alerts and transmission of selected signal samples. In someexamples, such an example method may be implemented by a system such as,for example, system 100 of FIG. 1.

At 400, real-time signals are received via a sensor at a productionstation, where the production station is associated with a dynamiccapacity limitation.

At 402, anomaly analysis is performed on real-time signals received by asensor, where the anomaly analysis utilizes an anomaly model and isperformed via an alerting system.

At 404, a selection of signal samples is identified based on the signalanalysis, where the selection is optimized at the production station tobe substantially relevant to an update of a statistical model whileadhering to the dynamic capacity limitation of the production station,and where the statistical model is maintained at a base facility linkedto the production station, and where the statistical model incorporatesfeatures related to the production station.

At 406, the selection is provided to the base facility, where the basefacility updates the statistical model based on the received selection,and derives an updated anomaly model based on the statistical model whenit is determined that the anomaly model is to be updated.

At 408, the updated anomaly model to perform anomaly analysis isreceived from the base facility, when it is determined that the capacitylimitation allows the production station to receive the updated anomalymodel, or the prior anomaly model is continued to be utilized.

FIG. 5 is a flow diagram illustrating one example of a method forproviding alerts at a production station. In some examples, such anexample method may be implemented by a system such as, for example,system 100 of FIG. 1.

At 500, real-time signals are received via a sensor at a productionstation, where the production station is associated with a dynamiccapacity limitation.

At 502, anomaly analysis is performed on real-time signals received by asensor, where the anomaly analysis utilizes an anomaly model and isperformed via an alerting system.

At 504, a selection of signal samples is identified based on the signalanalysis, where the selection is optimized at the production station tobe substantially relevant to an update of a statistical model whileadhering to the dynamic capacity limitation of the production station,and where the statistical model is maintained at a base facility linkedto the production station, and where the statistical model incorporatesfeatures related to the production station.

At 506, the selection is provided to the base facility, where the basefacility updates the statistical model based on the received selection,and derives an updated anomaly model based on the statistical model whenit is determined that the anomaly model is to be updated.

At 508, the updated anomaly model to perform anomaly analysis isreceived from the base facility, when it is determined that the capacitylimitation allows the production station to receive the updated anomalymodel, or the prior anomaly model is continued to be utilized.

At 510, the alerting system is utilized to apply anomaly patternalerting rules.

At 512, alerts are provided at the production station, where the alertsare generated based on the anomaly pattern alerting rules.

FIG. 6 is a flow diagram illustrating one example of a method forproviding an incrementally updated computer generated visual renditionof an anomaly analysis at a production station. In some examples, suchan example method may be implemented by a system such as, for example,system 100 of FIG. 1.

At 600, real-time signals are received via a sensor at a productionstation, where the production station is associated with a dynamiccapacity limitation.

At 602, anomaly analysis is performed on real-time signals received by asensor, where the anomaly analysis utilizes an anomaly model and isperformed via an alerting system.

At 604, a selection of signal samples is identified based on the signalanalysis, where the selection is optimized at the production station tobe substantially relevant to an update of a statistical model whileadhering to the dynamic capacity limitation of the production station,and where the statistical model is maintained at a base facility linkedto the production station, and where the statistical model incorporatesfeatures related to the production station.

At 606, the selection is provided to the base facility, where the basefacility updates the statistical model based on the received selection,and derives an updated anomaly model based on the statistical model whenit is determined that the anomaly model is to be updated.

At 608, the updated anomaly model to perform anomaly analysis isreceived from the base facility, when it is determined that the capacitylimitation allows the production station to receive the updated anomalymodel, or the prior anomaly model is continued to be utilized.

At 610, the alerting system is utilized to apply anomaly patternalerting rules based on an incrementally updated computer generatedvisual rendition of the anomaly analysis. For example, the incrementallyupdated computer generated visual rendition of the anomaly analysis maybe provided via a computing device at the production station. Anindication to tag a signal sample or anomaly event pattern as relevantmay be received via the computing device, and anomaly pattern alertingrules based on the indication may be generated.

At 612, alerts are provided at the production station, where the alertsare generated based on the anomaly pattern alerting rules.

FIG. 7 is a flow diagram illustrating one example of a method foroptimizing a selection of signal samples based on a representation ofthe signal samples by signal elements from a dictionary. In someexamples, such an example method may be implemented by a system such as,for example, system 100 of FIG. 1.

At 700, real-time signals are received via a sensor at a productionstation, where the production station is associated with a dynamiccapacity limitation.

At 702, anomaly analysis is performed on real-time signals received by asensor, where the anomaly analysis utilizes an anomaly model and isperformed via an alerting system.

At 704, a selection of signal samples is identified based on the signalanalysis, where the selection is optimized at the production station tobe substantially relevant to an update of a statistical model whileadhering to the dynamic capacity limitation of the production station,and where the statistical model is maintained at a base facility linkedto the production station, and where the statistical model incorporatesfeatures related to the production station.

At 706, the selection is provided to the base facility, where the basefacility updates the statistical model based on the received selection,and derives an updated anomaly model based on the statistical model whenit is determined that the anomaly model is to be updated.

At 708, the updated anomaly model to perform anomaly analysis isreceived from the base facility, when it is determined that the capacitylimitation allows the production station to receive the updated anomalymodel, or the prior anomaly model is continued to be utilized.

At 710, a dictionary of signal elements is received from a globalprocessing center. For example, the dictionary of signal elements may bereceived from the global processing center via the base facility, wherethe dictionary is generated by the global processing center based ondeep learning algorithms performed on a plurality of event patterns andsignal samples received from a plurality of production stations via aplurality of base facilities.

At 712, the selection may be optimized based on a representation of thesignal samples by the signal elements, where an error in therepresentation of the signal sample is small if the signal sample issubstantially represented as a combination of a small number of thesignal elements.

Examples of the disclosure provide a generalized system for real-timealerts and transmission of selected signal samples. The generalizedsystem provides a two- or three-tier distributed system framework thatenables remote locations with limited computation, storage, andnetworking resources to process high-rate measurement data streams inreal-time, in order to compute anomalies and other complex analytics forreal-time/forward looking alerting, and select the right level ofcompressed data representation to cache and/or transmit from the remotestations to the base, thereby mitigating the storage and/or bandwidthlimitations of the remote stations.

Although specific examples have been illustrated and described herein, avariety of alternate and/or equivalent implementations may besubstituted for the specific examples shown and described withoutdeparting from the scope of the present disclosure. This application isintended to cover any adaptations or variations of the specific examplesdiscussed herein.

The invention claimed is:
 1. A system comprising: a base facility thatanalyzes signal samples received from a production station linked to thebase facility, the production station having an alerting system toperform anomaly analysis on real-time signals received by a sensor atthe production station, and wherein the anomaly analysis utilizes amachine learning model trained to detect certain event patterns asanomaly patterns, the base facility including: a receiver to receive,from the production station, the signal samples based on the anomalyanalysis, wherein the signal samples are selected at the productionstation based on: an extent of an anomaly of the signal samples comparedto a statistical model, and a dynamic capacity limitation of theproduction station, and wherein the statistical model is maintained atthe base facility and incorporates features related to the productionstation; and a management system to: update the statistical model basedon the signal samples, derive an updated anomaly model based on thestatistical model upon a determination that the anomaly model is to beupdated, and transmit the updated anomaly model to the productionstation when the dynamic capacity limitation is determined to besufficient for the transmission.
 2. The system of claim 1, wherein themanagement system is to configure the alerting system to detect theanomaly patterns in the signal samples.
 3. The system of claim 2,wherein the statistical model includes an anomaly event patterns model,and the management system is to update the anomaly event patterns modelbased on the signal samples.
 4. The system of claim 3, wherein themanagement system is to: configure the alerting system to apply anomalypattern alerting rules to provide alerts at the production station, andupdate the alerting rules based on the updated anomaly event patternsmodel.
 5. The system of claim 4, comprising a graphical user interfaceto: provide, via a computing device at the base facility, anincrementally updated computer generated visual rendition of the signalsamples; receive, via the computing device, an indication to tag asignal sample or anomaly event pattern as relevant; and wherein theanomaly pattern alerting rules are based on the indication.
 6. Thesystem of claim 1, wherein the base facility is linked to a secondproduction station, and the management system is to update a secondstatistical model that incorporates features related to the secondproduction station, and wherein the update of the second statisticalmodel is based on second signal samples from the second productionstation, and wherein the second signal samples are selected at thesecond production station an extent of an anomaly of the signal samplescompared to the second statistical model, while adhering to a seconddynamic capacity limitation of the second production station.
 7. Thesystem of claim 1, wherein the dynamic capacity limitation includes oneof computing power, working memory capacity, long term storage capacity,network bandwidth, network failure, and network downtime.
 8. The systemof claim 1, wherein the base facility receives conditions data and themanagement system is to incorporate the conditions data into thestatistical model.
 9. The system of claim 1, wherein the base facilityis linked to a global processing center, and the receiver is to: receivea dictionary of signal elements from the global processing center,wherein the dictionary is generated by the global processing centerbased on deep learning algorithms performed on a plurality of eventpatterns and signal samples received from a plurality of productionstations via a plurality of base facilities; and provide the dictionaryto the production station; and wherein the production station selectsthe signal samples based on a representation of the signal samples bythe signal elements, wherein an error in the representation of a signalsample is small if the signal sample is substantially represented as acombination of a small number of the signal elements.
 10. The system ofclaim 1, wherein the dynamic capacity limitation includes an availablebandwidth, and the signal samples are selected such that, a lower anavailable bandwidth, a smaller an amount of the signal samples receivedat the base facility.
 11. The system of claim 1, wherein the signalsamples are cached at the production station based on an extent of ananomaly of the signal samples prior to the base facility receiving thesignal samples from the production station.
 12. The system of claim 1,wherein, in response to the base facility receiving the signal samplesfrom the production station, the cached signal samples are overwrittenat the base facility.
 13. The system of claim 1, wherein the machinelearning model is derived from the statistical model.
 14. The system ofclaim 1, wherein the signal samples have a higher extent of the anomalycompared to the statistical model than unselected signal samplescompared to the statistical model.
 15. A method comprising: receivingreal-time signals via a sensor at a production station, wherein theproduction station is associated with a dynamic capacity limitation;performing, via an alerting system, anomaly analysis on the real-timesignals received by the sensor, wherein the anomaly analysis utilizes amachine learning model trained to detect certain event patterns asanomaly patterns; identifying, at the production station, a selection ofsignal samples based on the anomaly analysis, wherein the selection isbased on: an extent of an anomaly of the signal samples compared to astatistical model, and the dynamic capacity limitation of the productionstation, and wherein the statistical model is: maintained at a basefacility linked to the production station, and incorporates featuresrelated to the production station; providing the selection to the basefacility, wherein the base facility updates the statistical model basedon the received selection, and derives an updated anomaly model based onthe statistical model upon a determination that the anomaly model is tobe updated; and receiving the updated anomaly model from the basefacility to perform anomaly analysis, when the dynamic capacitylimitation is determined to be sufficient for the production station toreceive the updated anomaly model, or continue to utilize a prioranomaly model.
 16. The method of claim 15, comprising: utilizing thealerting system to apply anomaly pattern alerting rules; and providingalerts at the production station, wherein the alerts are generated basedon the anomaly pattern alerting rules.
 17. The method of claim 16,comprising a graphical user interface to: provide, via a computingdevice at the production station, an incrementally updated computergenerated visual rendition of the anomaly analysis; receive, via thecomputing device, an indication to tag a signal sample or anomaly eventpattern as relevant; and generate anomaly pattern alerting rules basedon the indication.
 18. The method of claim 15, comprising: receiving adictionary of signal elements from a global processing center via thebase facility, wherein the dictionary is generated by the globalprocessing center based on deep learning algorithms performed on aplurality of event patterns and signal samples received from a pluralityof production stations via a plurality of base facilities; andoptimizing the selection based on a representation of the signal samplesby the signal elements, wherein an error in the representation of thesignal sample is small if the signal sample is substantially representedas a combination of a small number of the signal elements.
 19. Themethod of claim 18, wherein the dictionary is generated by the globalprocessing center linked to the base facility, and wherein thegenerating is based on deep learning algorithms performed on a pluralityof event patterns and signal samples received by the global processingcenter from a plurality of production stations via a plurality of basefacilities.
 20. A non-transitory computer readable medium comprisingexecutable instructions to: receive real-time signals via a sensor at aproduction station, wherein the production station is associated with adynamic capacity limitation; perform, via an alerting system, anomalyanalysis on the real-time signals received by the sensor, wherein theanomaly analysis utilizes a machine learning model trained to detectcertain event patterns as anomaly patterns; identify, at the productionstation, a selection of signal samples based on the anomaly analysis,wherein the selection is based on: an extent of an anomaly of the signalsamples compared to a statistical model, and the dynamic capacitylimitation of the production station, and wherein the statistical model:is maintained at a base facility linked to the production station, andincorporates features related to the production station; provide theselection to the base facility, wherein the base facility updates thestatistical model based on the received selection, and derives anupdated anomaly model based on the statistical model upon adetermination that the anomaly model is to be updated; receive theupdated anomaly model from the base facility to perform anomalyanalysis, when the dynamic capacity limitation is determined to besufficient for the production station to receive the updated anomalymodel; utilize the anomaly alerting rules to apply anomaly patternalerting rules; and provide alerts at the production station, whereinthe alerts are generated based on the anomaly pattern alerting rules.